Identification of Synthetic Survival and Synthetic Survival burden in multiple Cancer

Abstract

Background: According to cancer genomics become prevalent, large cancer genome projects are launched and almost finished. From these project, a huge amount of genomic, transcriptomic, epigenomic data is released in public. This public data have provided quite some studies of somatic mutations in cancer cell, then many researchers have identified several cancer-related genes. However, some existing cancer genomics studies have a limitations. Many studies are restricted to single cancer type or single cancer driver gene. It can limit studys scope to identify how cancer occur, develop and progress. To solve this problem, a concept of synthetic lethality is arisen. Synthetic lethality is a concept that a combination of mutations in two genes lead to cell death, while a mutation in an only one of these genes remains cell survival. We propose a concept synthetic survival based on the idea of synthetic lethality. Synthetic Survival (SS) is a concept that a combinations of mutations in two gene lead to patients survival, whereas a mutation in only one gene lead to patients bad prognosis. The goal of this dissertation is to identify synthetic survival and synthetic survival burden in multiple cancers. Method: We downloaded 9,184 patients clinical information and 6,936 patients somatic mutation data in 20 cancer types from The Cancer Genome Atlas. all of data is open-accessed data. To make core data set for further analysis, several criteria is applied to filter out data which is not able to analysis. All genes harboring at least one non-synonymous mutations are annotated to gene damaging score. Gene damaging score is a measure to quantify each genes deleteriousness. Gene damaging score is derived from both scores in Sorting Intolerant From Tolerant and classification of LoF mutations. By gene damaging score, patients are stratified to four groups. Survival analysis are conducted for all gene pairs group with cox proportional hazard model with penalized likelihood. From the cox model, candidates of potential synthetic survival pairs are decided. Result: The result from filtering raw data, we build the core data set for 4,844 patients including clinical information and somatic mutation profiles. For candidates of potential synthetic survival pairs, 436 gene pairs are identified in 5 cancer types. We also identified synthetic survival burden which means that the group which have more SS pairs and triplets have higher survival rate than the group which have less SS pairs Discussion: In this dissertation, Candidate SS pairs are identify by only performing survival analysis with cox proportional hazard model and also ensure that the number of SS pairs is related to patients prognosis. Genes consisting SS pairs are usually related to cell-cell interaction or migration so might be action to prevent metastasis, then shows good prognosis. This study might be approach to reduce cost for screening and to apply practical clinical uses.